Variables
#Data frames with all variables
#2009
dict09 = iccs09 %>%
mutate(time = 2009) %>%
mutate(country = COUNTRY) %>%
mutate(idcountry = IDCNTRY) %>%
mutate(idschool = IDSCHOOL) %>%
mutate(idstudent = IDSTUD) %>%
mutate(idstudent = (time*1000000000000 + idcountry*100000000 + idstudent)) %>% #NEW IDSTUDENT
#Authoritarianism
mutate(dicta1 = 5-LS2P02A) %>% #Government leaders to make decisions without consulting anybody
mutate(dicta2 = 5-LS2P02B) %>% #People in government must enforce their authority even
mutate(dicta3 = 5-LS2P02C) %>% #People in government lose part of their authority
mutate(dicta4 = 5-LS2P02D) %>% #People whose opinions are different must be considered its enemies
mutate(dicta5 = 5-LS2P02E) %>% #The most important opinion of a country should be that of the pres
mutate(dicta6 = 5-LS2P02F) %>% #It is fair that the government does not comply with the law
mutate(dicta7 = 5-LS2P03A) %>% #It is fair that the government does not comply with the law
mutate(dicta8 = 5-LS2P03B) %>% #Concentration of power in one person guarantees order
mutate(dicta9 = 5-LS2P03C) %>% #If the president does not agree withCongress>, he should dissolve
mutate(dicta_safety = 5-LS2P03D) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ORDER AND SAFETY
mutate(dicta_benefits = 5-LS2P03E) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ECONOMIC BENEFITS
mutate(dict = (dicta_safety + dicta_benefits)/2) %>% #MEAN DIC
#Dummies
mutate(dicta1_d = recode(dicta1, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta2_d = recode(dicta2, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta3_d = recode(dicta3, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta4_d = recode(dicta4, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta5_d = recode(dicta5, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta6_d = recode(dicta6, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta7_d = recode(dicta7, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta8_d = recode(dicta8, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta9_d = recode(dicta9, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta_saf_d = recode(dicta_safety, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta_ben_d = recode(dicta_benefits, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
#Civic Knowledge
mutate(pv1civ = PV1CIV) %>%
mutate(pv2civ = PV2CIV) %>%
mutate(pv3civ = PV3CIV) %>%
mutate(pv4civ = PV4CIV) %>%
mutate(pv5civ = PV5CIV) %>%
mutate(civic_knowledge = (pv1civ + pv2civ + pv3civ + pv4civ + pv5civ)/5) %>% #MEAN CIVIC KNOWLEDGE
#Independent Variables
mutate(s_opdisc = OPDISC) %>% #OPENNESS IN CLASS DISCUSSION
mutate(s_hisced = HISCED) %>% #HIGHEST PARENTAL EDUCATIONAL LEVEL
mutate(univ = ifelse(s_hisced>3, 1, 0)) %>% #UNIVERSITARY PARENTS
mutate(s_hisei = HISEI) %>% #PARENT'S HIGHEST OCCUPATIONAL STATUS
mutate(s_homelit = HOMELIT) %>% #HOME LITERACY
mutate(s_gender = SGENDER) %>% #GENDER OF STUDENT
mutate(s_age = SAGE) %>% #AGE STUDENT
mutate(s_citcon = CITCON) %>% #CONVENTIONAL CITIZENSHIP
mutate(s_citsoc = CITSOC) %>% #SOCIAL MOVEMENT REL. CITIZENSHIP
mutate(s_citeff = CITEFF) %>% #CITIZENSHIP SELF-EFFICACY
mutate(s_cntatt = ATTCNT) %>% #ATTITUDES TOWARDS OWN COUNTRY
mutate(s_geneql = GENEQL) %>% #ATTITUDES TOWARDS GENDER EQUALITY
mutate(s_ethrght = ETHRGHT) %>% #EQUAL RIGHTS FOR ALL ETHNIC GROUPS
mutate(l_attviol = ATTVIOL) %>% #ATTITUDES: USE OF VIOLENCE
mutate(l_attdiv = ATTDIFF) %>% #ATTITUDES: NEIGHBOURHOOD DIVERSITY
mutate(l_autgov = AUTGOV) %>% #AUTHORITARIANISM IN GOVERNMENT
mutate(l_attcorr = ATTCORR) %>% #CORRUPT PRACTICES IN GOVERNMENT
mutate(l_dislaw = DISLAW) %>% #ATTITUDES: DISOBEYING THE LAW
mutate(l_empclas = EMPATH) %>% #EMPATHY TOWARDS CLASSMATES
mutate(s_poldisc = POLDISC) %>% #DISCUSSION OF POL. AND SOC. ISSUES
#TRUST
mutate(s_intrust = INTRUST) %>% #TRUST IN CIVIC INSTITUTIONS
mutate(nac_gob = 5 - IS2P27A) %>% #TRUST INSTITUTIONS-NATIONAL GOVERNMENT
mutate(local_gob = 5 - IS2P27B) %>% #TRUST INSTITUTIONS-LOCAL GOVERNMENT
mutate(courts = 5 - IS2P27C) %>% #TRUST INSTITUTIONS-COURTS
mutate(police = 5 - IS2P27D) %>% #TRUST INSTITUTIONS-POLICE
mutate(pol_parties = 5 - IS2P27E) %>% #TRUST INSTITUTIONS-POLITICAL PARTIES
mutate(parliament = 5 - IS2P27F) %>% #TRUST INSTITUTIONS-PARLIAMENT
mutate(media = 5 - IS2P27G) %>% #TRUST INSTITUTIONS-MEDIA
mutate(ffaa = 5 - IS2P27H) %>% #TRUST INSTITUTIONS-FFAA
mutate(school = 5 - IS2P27I) %>% #TRUST INSTITUTIONS-SCHOOL
mutate(unit_nations = 5 - IS2P27J) %>% #TRUST INSTITUTIONS-UNITED NATIONS
mutate(people = 5 - IS2P27K) %>% #TRUST INSTITUTIONS-PEOPLE
mutate(social = NA) %>% #TRUST INSTITUTIONS-SOCIAL MEDIA
#Dummies
mutate(nac_gob_d = recode(nac_gob, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(local_gob_d = recode(local_gob, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(courts_d = recode(courts, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(police_d = recode(police, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(pol_parties_d = recode(pol_parties, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(parliament_d = recode(parliament, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(media_d = recode(media, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(ffaa_d = recode(ffaa, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(school_d = recode(school, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(unit_nations_d = recode(unit_nations, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(people_d = recode(people, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(social_d = NA) %>%
#WEITHINGS5 -
mutate(totwgts = TOTWGTS) %>% #FINAL STUDENT WEIGHT
mutate(wgtfac1 = WGTFAC1) %>% #SCHOOL BASE WEIGHT
mutate(wgtadj1s = WGTADJ1S) %>% #SCHOOL WEIGHT ADJUSTMENT-STUDENT STUDY
mutate(wgtfac2s = WGTFAC2S) %>% #CLASS WEIGHT FACTOR
mutate(wgtadj2s = WGTADJ2S) %>% #CLASS WEIGHT ADJUSTMENT
mutate(wgtadj3s = WGTADJ3S) %>% #STUDENT WEIGHT ADJUSTMENT
mutate(jkzones = JKZONES) %>% #JACKKNIFE ZONE - STUDENT STUDY
mutate(jkreps = JKREPS) %>% #JACKKNIFE REPLICATE CODE
select(411:497)
#2016
dict16 = iccs16 %>%
mutate(time = 2016) %>%
mutate(country = COUNTRY) %>%
mutate(idcountry = IDCNTRY) %>%
mutate(idschool = IDSCHOOL) %>%
mutate(idstudent = IDSTUD) %>%
mutate(idstudent = (time*100000000000 + idcountry*100000000 + idstudent)) %>% #NEW IDSTUDENT
#Authoritarianism
mutate(dicta1 = 5-LS3G01A) %>% #Government leaders to make decisions without consulting anybody
mutate(dicta2 = 5-LS3G01B) %>% #People in government must enforce their authority even
mutate(dicta3 = 5-LS3G01C) %>% #People in government lose part of their authority
mutate(dicta4 = 5-LS3G01D) %>% #People whose opinions are different must be considered its enemies
mutate(dicta5 = 5-LS3G01E) %>% #The most important opinion of a country should be that of the pres
mutate(dicta6 = 5-LS3G01F) %>% #It is fair that the government does not comply with the law
mutate(dicta7 = 5-LS3G02A) %>% #It is fair that the government does not comply with the law
mutate(dicta8 = 5-LS3G02B) %>% #Concentration of power in one person guarantees order
mutate(dicta9 = 5-LS3G02C) %>% #If the president does not agree withCongress>, he should dissolve
mutate(dicta_safety = 5-LS3G02D) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ORDER AND SAFETY
mutate(dicta_benefits = 5-LS3G02E) %>% #DICTATORSHIPS ARE JUSTIFIED WHEN THEY BRING ECONOMIC BENEFITS
mutate(dict = (dicta_safety + dicta_benefits)/2) %>%
#Dummies
mutate(dicta1_d = recode(dicta1, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta2_d = recode(dicta2, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta3_d = recode(dicta3, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta4_d = recode(dicta4, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta5_d = recode(dicta5, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta6_d = recode(dicta6, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta7_d = recode(dicta7, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta8_d = recode(dicta8, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta9_d = recode(dicta9, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta_saf_d = recode(dicta_safety, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(dicta_ben_d = recode(dicta_benefits, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
#Civic Knowledge
mutate(pv1civ = PV1CIV) %>%
mutate(pv2civ = PV2CIV) %>%
mutate(pv3civ = PV3CIV) %>%
mutate(pv4civ = PV4CIV) %>%
mutate(pv5civ = PV5CIV) %>%
mutate(civic_knowledge = (pv1civ + pv2civ + pv3civ + pv4civ + pv5civ)/5) %>% #MEAN CIVIC KNOWLEDGE
#Independent Variables
mutate(s_opdisc = S_OPDISC) %>% #OPENNESS IN CLASS DISCUSSION
mutate(s_hisced = S_HISCED) %>% #HIGHEST PARENTAL EDUCATIONAL LEVEL
mutate(univ = ifelse(s_hisced>3, 1, 0)) %>% #UNIVERSITARY PARENTS
mutate(s_hisei = S_HISEI) %>% #PARENT'S HIGHEST OCCUPATIONAL STATUS
mutate(s_homelit = S_HOMLIT) %>% #HOME LITERACY
mutate(s_gender = S_GENDER) %>% #GENDER OF STUDENT
mutate(s_age = S_AGE) %>% #AGE STUDENT
mutate(s_citcon = S_CITCON) %>% #CONVENTIONAL CITIZENSHIP
mutate(s_citsoc = S_CITSOC) %>% #SOCIAL MOVEMENT REL. CITIZENSHIP
mutate(s_citeff = S_CITEFF) %>% #CITIZENSHIP SELF-EFFICACY
mutate(s_cntatt = S_CNTATT) %>% #ATTITUDES TOWARDS OWN COUNTRY
mutate(s_geneql = S_GENEQL) %>% #ATTITUDES TOWARDS GENDER EQUALITY
mutate(s_ethrght = S_ETHRGHT) %>% #EQUAL RIGHTS FOR ALL ETHNIC GROUPS
mutate(l_attviol = L_ATTVIOL) %>% #ATTITUDES: USE OF VIOLENCE
mutate(l_attdiv = L_ATTDIV) %>% #ATTITUDES: NEIGHBOURHOOD DIVERSITY
mutate(l_autgov = L_AUTGOV) %>% #AUTHORITARIANISM IN GOVERNMENT
mutate(l_attcorr = L_ATTCORR) %>% #CORRUPT PRACTICES IN GOVERNMENT
mutate(l_dislaw = L_DISLAW) %>% #ATTITUDES: DISOBEYING THE LAW
mutate(l_empclas = L_EMPCLAS) %>% #EMPATHY TOWARDS CLASSMATES
mutate(s_poldisc = S_POLDISC) %>% #DISCUSSION OF POL. AND SOC. ISSUES
#TRUST
mutate(s_intrust = S_INTRUST) %>% #TRUST IN CIVIC INSTITUTIONS
mutate(nac_gob = 5 - IS3G26A) %>% #TRUST INSTITUTIONS-NATIONAL GOVERNMENT
mutate(local_gob = 5 - IS3G26B) %>% #TRUST INSTITUTIONS-LOCAL GOVERNMENT
mutate(courts = 5 - IS3G26C) %>% #TRUST INSTITUTIONS-COURTS
mutate(police = 5 - IS3G26D) %>% #TRUST INSTITUTIONS-POLICE
mutate(pol_parties = 5 - IS3G26E) %>% #TRUST INSTITUTIONS-POLITICAL PARTIES
mutate(parliament = 5 - IS3G26F) %>% #TRUST INSTITUTIONS-PARLIAMENT
mutate(media = 5 - IS3G26G) %>% #TRUST INSTITUTIONS-MEDIA
mutate(ffaa = 5 - IS3G26I) %>% #TRUST INSTITUTIONS-FFAA
mutate(school = 5 - IS3G26J) %>% #TRUST INSTITUTIONS-SCHOOL
mutate(unit_nations = 5 - IS3G26K) %>% #TRUST INSTITUTIONS-UNITED NATIONS
mutate(people = 5 - IS3G26L) %>% #TRUST INSTITUTIONS-PEOPLE
mutate(social = 5 - IS3G26H) %>% #TRUST INSTITUTIONS-SOCIAL MEDIA
#Dummies
mutate(nac_gob_d = recode(nac_gob, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(local_gob_d = recode(local_gob, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(courts_d = recode(courts, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(police_d = recode(police, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(pol_parties_d = recode(pol_parties, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(parliament_d = recode(parliament, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(media_d = recode(media, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(ffaa_d = recode(ffaa, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(school_d = recode(school, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(unit_nations_d = recode(unit_nations,"1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(people_d = recode(people, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
mutate(social_d = recode(social, "1"=0, "2"=0, "3"=1, "4"=1)) %>%
#WEITHINGS5 -
mutate(totwgts = TOTWGTS) %>% #FINAL STUDENT WEIGHT
mutate(wgtfac1 = WGTFAC1) %>% #SCHOOL BASE WEIGHT
mutate(wgtadj1s = WGTADJ1S) %>% #SCHOOL WEIGHT ADJUSTMENT-STUDENT STUDY
mutate(wgtfac2s = WGTFAC2S) %>% #CLASS WEIGHT FACTOR
mutate(wgtadj2s = WGTADJ2S) %>% #CLASS WEIGHT ADJUSTMENT
mutate(wgtadj3s = WGTADJ3S) %>% #STUDENT WEIGHT ADJUSTMENT
mutate(jkzones = JKZONES) %>% #JACKKNIFE ZONE - STUDENT STUDY
mutate(jkreps = JKREPS) %>% #JACKKNIFE REPLICATE CODE
select(519:605)
#Merge data
mergeiccs <- full_join(dict09, dict16)
## Joining, by = c("time", "country", "idcountry", "idschool", "idstudent", "dicta1", "dicta2", "dicta3", "dicta4", "dicta5", "dicta6", "dicta7", "dicta8", "dicta9", "dicta_safety", "dicta_benefits", "dict", "dicta1_d", "dicta2_d", "dicta3_d", "dicta4_d", "dicta5_d", "dicta6_d", "dicta7_d", "dicta8_d", "dicta9_d", "dicta_saf_d", "dicta_ben_d", "pv1civ", "pv2civ", "pv3civ", "pv4civ", "pv5civ", "civic_knowledge", "s_opdisc", "s_hisced", "univ", "s_hisei", "s_homelit", "s_gender", "s_age", "s_citcon", "s_citsoc", "s_citeff", "s_cntatt", "s_geneql", "s_ethrght", "l_attviol", "l_attdiv", "l_autgov", "l_attcorr", "l_dislaw", "l_empclas", "s_poldisc", "s_intrust", "nac_gob", "local_gob", "courts", "police", "pol_parties", "parliament", "media", "ffaa", "school", "unit_nations", "people", "social", "nac_gob_d", "local_gob_d", "courts_d", "police_d", "pol_parties_d", "parliament_d", "media_d", "ffaa_d", "school_d", "unit_nations_d", "people_d", "social_d", "totwgts", "wgtfac1", "wgtadj1s", "wgtfac2s", "wgtadj2s", "wgtadj3s", "jkzones", "jkreps")
Sample Size
#ICCS LA: 2009 - 2016
iccs_count <- mergeiccs %>%
group_by(time, ncountry) %>%
dplyr::summarise(N=n(), man=mean(s_gender, na.rm=T), age=mean(s_age, na.rm=T)) %>%
arrange(ncountry)
#ID School
iccs_count2 <- mergeiccs %>%
group_by(time, ncountry, idschool) %>%
dplyr::summarise(n=n()) %>%
dplyr::summarise(s=n()) %>%
arrange(ncountry)
iccs_count$s <- iccs_count2$s
rm(iccs_count2)
iccs_count$time <- as.character(iccs_count$time)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
kable(iccs_count, align = c("lcccccc")) %>%
kable_styling(bootstrap_options = c("striped", "hover"))
|
time
|
ncountry
|
N
|
man
|
age
|
s
|
|
2009
|
Chile
|
5173
|
0.5143801
|
14.17872
|
177
|
|
2016
|
Chile
|
5081
|
0.4928164
|
14.17268
|
178
|
|
2009
|
Colombia
|
6200
|
0.5352932
|
14.37904
|
196
|
|
2016
|
Colombia
|
5609
|
0.5229096
|
14.59167
|
150
|
|
2009
|
Dominican Republic
|
4569
|
0.5468819
|
14.85543
|
145
|
|
2016
|
Dominican Republic
|
3937
|
0.5128270
|
14.18576
|
141
|
|
2009
|
Guatemala
|
3998
|
0.4899699
|
15.51745
|
145
|
|
2009
|
Mexico
|
6565
|
0.5222493
|
14.08043
|
215
|
|
2016
|
Mexico
|
5526
|
0.5000000
|
14.03109
|
213
|
|
2009
|
Paraguay
|
3391
|
0.5213801
|
14.81850
|
149
|
|
2016
|
Peru
|
5166
|
0.4816105
|
14.03021
|
206
|
Institutional Trust
#Design
mergesvy <- mergeiccs %>%
as_survey_design(
strata = jkzones,
weights = totwgts,
ids = jkreps,
nest = TRUE)
#Adjust and table
mergesvy[["variables"]][["nac_gob_d"]] <- as.character(mergesvy[["variables"]][["nac_gob_d"]])
mergesvy[["variables"]][["police_d"]] <- as.character(mergesvy[["variables"]][["police_d"]])
mergesvy[["variables"]][["pol_parties_d"]] <- as.character(mergesvy[["variables"]][["pol_parties_d"]])
mergesvy[["variables"]][["people_d"]] <- as.character(mergesvy[["variables"]][["people_d"]])
#########################
#National Goberment
#########################
table_freq_01 <- mergesvy %>%
dplyr::group_by(country, time, nac_gob_d) %>%
summarize(proportion = survey_mean(,na.rm=TRUE, "ci"))
#Table
#print(xtable(table_freq_01, caption = "Sample", format="text"), include.rownames=FALSE)
kable(table_freq_01, align = c("lcccccc")) %>%
kable_styling(bootstrap_options = c("striped", "hover"))
|
country
|
time
|
nac_gob_d
|
proportion
|
proportion_low
|
proportion_upp
|
|
CHL
|
2009
|
0
|
0.3486123
|
0.3291055
|
0.3681191
|
|
CHL
|
2009
|
1
|
0.6513877
|
0.6318809
|
0.6708945
|
|
CHL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
CHL
|
2016
|
0
|
0.5041627
|
0.4847861
|
0.5235392
|
|
CHL
|
2016
|
1
|
0.4958373
|
0.4764608
|
0.5152139
|
|
CHL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2009
|
0
|
0.3797472
|
0.3552204
|
0.4042741
|
|
COL
|
2009
|
1
|
0.6202528
|
0.5957259
|
0.6447796
|
|
COL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2016
|
0
|
0.4476026
|
0.4236968
|
0.4715084
|
|
COL
|
2016
|
1
|
0.5523974
|
0.5284916
|
0.5763032
|
|
COL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2009
|
0
|
0.2612638
|
0.2365092
|
0.2860184
|
|
DOM
|
2009
|
1
|
0.7387362
|
0.7139816
|
0.7634908
|
|
DOM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2016
|
0
|
0.2219649
|
0.1992088
|
0.2447210
|
|
DOM
|
2016
|
1
|
0.7780351
|
0.7552790
|
0.8007912
|
|
DOM
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
GTM
|
2009
|
0
|
0.5469156
|
0.5200227
|
0.5738086
|
|
GTM
|
2009
|
1
|
0.4530844
|
0.4261914
|
0.4799773
|
|
GTM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2009
|
0
|
0.4158023
|
0.3954032
|
0.4362014
|
|
MEX
|
2009
|
1
|
0.5841977
|
0.5637986
|
0.6045968
|
|
MEX
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2016
|
0
|
0.4299568
|
0.4090000
|
0.4509136
|
|
MEX
|
2016
|
1
|
0.5700432
|
0.5490864
|
0.5910000
|
|
MEX
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PER
|
2016
|
0
|
0.5096989
|
0.4893269
|
0.5300710
|
|
PER
|
2016
|
1
|
0.4903011
|
0.4699290
|
0.5106731
|
|
PER
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PRY
|
2009
|
0
|
0.3409936
|
0.3156542
|
0.3663329
|
|
PRY
|
2009
|
1
|
0.6590064
|
0.6336671
|
0.6843458
|
|
PRY
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
country
|
time
|
police_d
|
proportion
|
proportion_low
|
proportion_upp
|
|
CHL
|
2009
|
0
|
0.2896503
|
0.2717131
|
0.3075874
|
|
CHL
|
2009
|
1
|
0.7103497
|
0.6924126
|
0.7282869
|
|
CHL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
CHL
|
2016
|
0
|
0.3551635
|
0.3366103
|
0.3737167
|
|
CHL
|
2016
|
1
|
0.6448365
|
0.6262833
|
0.6633897
|
|
CHL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2009
|
0
|
0.4507952
|
0.4293250
|
0.4722654
|
|
COL
|
2009
|
1
|
0.5492048
|
0.5277346
|
0.5706750
|
|
COL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2016
|
0
|
0.5076088
|
0.4828832
|
0.5323344
|
|
COL
|
2016
|
1
|
0.4923912
|
0.4676656
|
0.5171168
|
|
COL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2009
|
0
|
0.4369578
|
0.4107802
|
0.4631354
|
|
DOM
|
2009
|
1
|
0.5630422
|
0.5368646
|
0.5892198
|
|
DOM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2016
|
0
|
0.4393891
|
0.4152856
|
0.4634927
|
|
DOM
|
2016
|
1
|
0.5606109
|
0.5365073
|
0.5847144
|
|
DOM
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
GTM
|
2009
|
0
|
0.6664363
|
0.6423384
|
0.6905341
|
|
GTM
|
2009
|
1
|
0.3335637
|
0.3094659
|
0.3576616
|
|
GTM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2009
|
0
|
0.5699461
|
0.5528957
|
0.5869966
|
|
MEX
|
2009
|
1
|
0.4300539
|
0.4130034
|
0.4471043
|
|
MEX
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2016
|
0
|
0.5097115
|
0.4933779
|
0.5260450
|
|
MEX
|
2016
|
1
|
0.4902885
|
0.4739550
|
0.5066221
|
|
MEX
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PER
|
2016
|
0
|
0.4978979
|
0.4810746
|
0.5147213
|
|
PER
|
2016
|
1
|
0.5021021
|
0.4852787
|
0.5189254
|
|
PER
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PRY
|
2009
|
0
|
0.5494438
|
0.5270717
|
0.5718159
|
|
PRY
|
2009
|
1
|
0.4505562
|
0.4281841
|
0.4729283
|
|
PRY
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
country
|
time
|
pol_parties_d
|
proportion
|
proportion_low
|
proportion_upp
|
|
CHL
|
2009
|
0
|
0.6550144
|
0.6346769
|
0.6753518
|
|
CHL
|
2009
|
1
|
0.3449856
|
0.3246482
|
0.3653231
|
|
CHL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
CHL
|
2016
|
0
|
0.6745075
|
0.6587782
|
0.6902368
|
|
CHL
|
2016
|
1
|
0.3254925
|
0.3097632
|
0.3412218
|
|
CHL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2009
|
0
|
0.6506877
|
0.6295206
|
0.6718549
|
|
COL
|
2009
|
1
|
0.3493123
|
0.3281451
|
0.3704794
|
|
COL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2016
|
0
|
0.7228073
|
0.7029239
|
0.7426906
|
|
COL
|
2016
|
1
|
0.2771927
|
0.2573094
|
0.2970761
|
|
COL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2009
|
0
|
0.4887925
|
0.4644688
|
0.5131161
|
|
DOM
|
2009
|
1
|
0.5112075
|
0.4868839
|
0.5355312
|
|
DOM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2016
|
0
|
0.5024324
|
0.4798019
|
0.5250629
|
|
DOM
|
2016
|
1
|
0.4975676
|
0.4749371
|
0.5201981
|
|
DOM
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
GTM
|
2009
|
0
|
0.7368829
|
0.7169853
|
0.7567805
|
|
GTM
|
2009
|
1
|
0.2631171
|
0.2432195
|
0.2830147
|
|
GTM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2009
|
0
|
0.6535926
|
0.6329986
|
0.6741866
|
|
MEX
|
2009
|
1
|
0.3464074
|
0.3258134
|
0.3670014
|
|
MEX
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2016
|
0
|
0.6261329
|
0.6059780
|
0.6462878
|
|
MEX
|
2016
|
1
|
0.3738671
|
0.3537122
|
0.3940220
|
|
MEX
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PER
|
2016
|
0
|
0.6660155
|
0.6471337
|
0.6848973
|
|
PER
|
2016
|
1
|
0.3339845
|
0.3151027
|
0.3528663
|
|
PER
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PRY
|
2009
|
0
|
0.6755932
|
0.6567009
|
0.6944855
|
|
PRY
|
2009
|
1
|
0.3244068
|
0.3055145
|
0.3432991
|
|
PRY
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
country
|
time
|
people_d
|
proportion
|
proportion_low
|
proportion_upp
|
|
CHL
|
2009
|
0
|
0.4825848
|
0.4652249
|
0.4999446
|
|
CHL
|
2009
|
1
|
0.5174152
|
0.5000554
|
0.5347751
|
|
CHL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
CHL
|
2016
|
0
|
0.5222508
|
0.5053029
|
0.5391987
|
|
CHL
|
2016
|
1
|
0.4777492
|
0.4608013
|
0.4946971
|
|
CHL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2009
|
0
|
0.5116344
|
0.4933059
|
0.5299628
|
|
COL
|
2009
|
1
|
0.4883656
|
0.4700372
|
0.5066941
|
|
COL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2016
|
0
|
0.5651447
|
0.5430788
|
0.5872106
|
|
COL
|
2016
|
1
|
0.4348553
|
0.4127894
|
0.4569212
|
|
COL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2009
|
0
|
0.3897426
|
0.3636206
|
0.4158645
|
|
DOM
|
2009
|
1
|
0.6102574
|
0.5841355
|
0.6363794
|
|
DOM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2016
|
0
|
0.3801493
|
0.3586919
|
0.4016067
|
|
DOM
|
2016
|
1
|
0.6198507
|
0.5983933
|
0.6413081
|
|
DOM
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
GTM
|
2009
|
0
|
0.5258643
|
0.5041887
|
0.5475399
|
|
GTM
|
2009
|
1
|
0.4741357
|
0.4524601
|
0.4958113
|
|
GTM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2009
|
0
|
0.5341219
|
0.5191580
|
0.5490859
|
|
MEX
|
2009
|
1
|
0.4658781
|
0.4509141
|
0.4808420
|
|
MEX
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2016
|
0
|
0.4829065
|
0.4638750
|
0.5019381
|
|
MEX
|
2016
|
1
|
0.5170935
|
0.4980619
|
0.5361250
|
|
MEX
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PER
|
2016
|
0
|
0.5284377
|
0.5113651
|
0.5455104
|
|
PER
|
2016
|
1
|
0.4715623
|
0.4544896
|
0.4886349
|
|
PER
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PRY
|
2009
|
0
|
0.4276067
|
0.4082165
|
0.4469968
|
|
PRY
|
2009
|
1
|
0.5723933
|
0.5530032
|
0.5917835
|
|
PRY
|
2009
|
NA
|
NA
|
NA
|
NA
|
Support authoritarianism
|
country
|
time
|
dicta_ben_d
|
proportion
|
proportion_low
|
proportion_upp
|
|
CHL
|
2009
|
0
|
0.3599497
|
0.3400556
|
0.3798438
|
|
CHL
|
2009
|
1
|
0.6400503
|
0.6201562
|
0.6599444
|
|
CHL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
CHL
|
2016
|
0
|
0.4844084
|
0.4650626
|
0.5037541
|
|
CHL
|
2016
|
1
|
0.5155916
|
0.4962459
|
0.5349374
|
|
CHL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2009
|
0
|
0.3004007
|
0.2878003
|
0.3130012
|
|
COL
|
2009
|
1
|
0.6995993
|
0.6869988
|
0.7121997
|
|
COL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2016
|
0
|
0.3248039
|
0.3035032
|
0.3461047
|
|
COL
|
2016
|
1
|
0.6751961
|
0.6538953
|
0.6964968
|
|
COL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2009
|
0
|
0.3398834
|
0.3180054
|
0.3617615
|
|
DOM
|
2009
|
1
|
0.6601166
|
0.6382385
|
0.6819946
|
|
DOM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2016
|
0
|
0.2997769
|
0.2781407
|
0.3214131
|
|
DOM
|
2016
|
1
|
0.7002231
|
0.6785869
|
0.7218593
|
|
DOM
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
GTM
|
2009
|
0
|
0.2540651
|
0.2357930
|
0.2723373
|
|
GTM
|
2009
|
1
|
0.7459349
|
0.7276627
|
0.7642070
|
|
GTM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2009
|
0
|
0.3382672
|
0.3235268
|
0.3530077
|
|
MEX
|
2009
|
1
|
0.6617328
|
0.6469923
|
0.6764732
|
|
MEX
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2016
|
0
|
0.3357496
|
0.3159656
|
0.3555335
|
|
MEX
|
2016
|
1
|
0.6642504
|
0.6444665
|
0.6840344
|
|
MEX
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PER
|
2016
|
0
|
0.2820944
|
0.2654873
|
0.2987014
|
|
PER
|
2016
|
1
|
0.7179056
|
0.7012986
|
0.7345127
|
|
PER
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PRY
|
2009
|
0
|
0.3533853
|
0.3347247
|
0.3720458
|
|
PRY
|
2009
|
1
|
0.6466147
|
0.6279542
|
0.6652753
|
|
PRY
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
country
|
time
|
dicta_saf_d
|
proportion
|
proportion_low
|
proportion_upp
|
|
CHL
|
2009
|
0
|
0.3473609
|
0.3264615
|
0.3682604
|
|
CHL
|
2009
|
1
|
0.6526391
|
0.6317396
|
0.6735385
|
|
CHL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
CHL
|
2016
|
0
|
0.4344875
|
0.4130932
|
0.4558818
|
|
CHL
|
2016
|
1
|
0.5655125
|
0.5441182
|
0.5869068
|
|
CHL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2009
|
0
|
0.2604319
|
0.2465840
|
0.2742798
|
|
COL
|
2009
|
1
|
0.7395681
|
0.7257202
|
0.7534160
|
|
COL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2016
|
0
|
0.2731401
|
0.2575872
|
0.2886929
|
|
COL
|
2016
|
1
|
0.7268599
|
0.7113071
|
0.7424128
|
|
COL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2009
|
0
|
0.2953395
|
0.2751511
|
0.3155278
|
|
DOM
|
2009
|
1
|
0.7046605
|
0.6844722
|
0.7248489
|
|
DOM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2016
|
0
|
0.2653299
|
0.2474511
|
0.2832088
|
|
DOM
|
2016
|
1
|
0.7346701
|
0.7167912
|
0.7525489
|
|
DOM
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
GTM
|
2009
|
0
|
0.2154922
|
0.1987022
|
0.2322822
|
|
GTM
|
2009
|
1
|
0.7845078
|
0.7677178
|
0.8012978
|
|
GTM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2009
|
0
|
0.3148958
|
0.2981412
|
0.3316504
|
|
MEX
|
2009
|
1
|
0.6851042
|
0.6683496
|
0.7018588
|
|
MEX
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2016
|
0
|
0.3291197
|
0.3098661
|
0.3483732
|
|
MEX
|
2016
|
1
|
0.6708803
|
0.6516268
|
0.6901339
|
|
MEX
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PER
|
2016
|
0
|
0.2281320
|
0.2112895
|
0.2449745
|
|
PER
|
2016
|
1
|
0.7718680
|
0.7550255
|
0.7887105
|
|
PER
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PRY
|
2009
|
0
|
0.3052552
|
0.2857836
|
0.3247268
|
|
PRY
|
2009
|
1
|
0.6947448
|
0.6752732
|
0.7142164
|
|
PRY
|
2009
|
NA
|
NA
|
NA
|
NA
|
Latin American 2009-2016
Support Authoritarianism



## quartz_off_screen
## 2
## quartz_off_screen
## 2
Institutuional Trust








## quartz_off_screen
## 2
## quartz_off_screen
## 2
## quartz_off_screen
## 2
## quartz_off_screen
## 2
## quartz_off_screen
## 2
Interaction
#Modelos de regresión
#VD: Trust
#VD: Support authoritarianism
#VI: Trust
#VC: Nivel educacional de los padres, Libros en la casa, género del estudiante, nivel de discusión política, cohortes
##########################################################3
#OLS
##########################################################3
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=mergeiccs, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mergeiccs, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mergeiccs, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=mergeiccs, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mergeiccs, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
75.87***
|
-178.68***
|
-180.92***
|
|
|
(0.32)
|
(23.00)
|
(23.01)
|
|
s_intrust
|
0.17***
|
0.17***
|
0.26***
|
|
|
(0.00)
|
(0.00)
|
(0.02)
|
|
civic_knowledge
|
-0.08***
|
-0.08***
|
-0.07***
|
|
|
(0.00)
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
0.03
|
0.03
|
|
|
|
(0.03)
|
(0.03)
|
|
s_homelit
|
|
-0.19***
|
-0.19***
|
|
|
|
(0.04)
|
(0.04)
|
|
s_gender
|
|
-1.16***
|
-1.16***
|
|
|
|
(0.08)
|
(0.08)
|
|
s_poldisc
|
|
-0.02***
|
-0.02***
|
|
|
|
(0.00)
|
(0.00)
|
|
time
|
|
0.13***
|
0.13***
|
|
|
|
(0.01)
|
(0.01)
|
|
s_intrust:civic_knowledge
|
|
|
-0.00***
|
|
|
|
|
(0.00)
|
|
R2
|
0.39
|
0.39
|
0.39
|
|
Adj. R2
|
0.39
|
0.39
|
0.39
|
|
Num. obs.
|
51794
|
50281
|
50281
|
|
RMSE
|
97.78
|
97.69
|
97.67
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
63.10***
|
0.07
|
|
|
(0.26)
|
(27.35)
|
|
civic_knowledge
|
-0.03***
|
-0.03***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
0.03
|
|
|
|
(0.03)
|
|
s_homelit
|
|
-0.31***
|
|
|
|
(0.04)
|
|
s_gender
|
|
-1.04***
|
|
|
|
(0.09)
|
|
s_poldisc
|
|
0.12***
|
|
|
|
(0.00)
|
|
time
|
|
0.03*
|
|
|
|
(0.01)
|
|
R2
|
0.05
|
0.07
|
|
Adj. R2
|
0.05
|
0.07
|
|
Num. obs.
|
51954
|
50420
|
|
RMSE
|
117.01
|
116.37
|
|
p < 0.001, p < 0.01, p < 0.05
|
#Plot
library(sjPlot)
png(file = "~/Dropbox/book_authoritarianism/Resultados/graph/g8.png", height = 300)
plot <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
dev.off()
quartz_off_screen 2

plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
plot2

OLS by chile
##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))
chi <- mergeiccs %>% filter(idcountry == 152)
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=chi, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=chi, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=chi, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=chi, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=chi, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
72.44***
|
793.05***
|
792.12***
|
|
|
(0.71)
|
(56.41)
|
(56.36)
|
|
s_intrust
|
0.20***
|
0.18***
|
0.37***
|
|
|
(0.01)
|
(0.01)
|
(0.05)
|
|
civic_knowledge
|
-0.07***
|
-0.07***
|
-0.05***
|
|
|
(0.00)
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
-0.02
|
-0.02
|
|
|
|
(0.09)
|
(0.09)
|
|
s_homelit
|
|
-0.14
|
-0.14
|
|
|
|
(0.09)
|
(0.09)
|
|
s_gender
|
|
-0.67***
|
-0.67***
|
|
|
|
(0.18)
|
(0.18)
|
|
s_poldisc
|
|
0.01
|
0.01
|
|
|
|
(0.01)
|
(0.01)
|
|
time
|
|
-0.36***
|
-0.36***
|
|
|
|
(0.03)
|
(0.03)
|
|
s_intrust:civic_knowledge
|
|
|
-0.00***
|
|
|
|
|
(0.00)
|
|
R2
|
0.36
|
0.38
|
0.38
|
|
Adj. R2
|
0.36
|
0.38
|
0.38
|
|
Num. obs.
|
10071
|
9855
|
9855
|
|
RMSE
|
62.81
|
62.23
|
62.18
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
56.49***
|
949.93***
|
|
|
(0.61)
|
(64.44)
|
|
civic_knowledge
|
-0.02***
|
-0.02***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
-0.08
|
|
|
|
(0.11)
|
|
s_homelit
|
|
-0.21*
|
|
|
|
(0.10)
|
|
s_gender
|
|
-0.86***
|
|
|
|
(0.21)
|
|
s_poldisc
|
|
0.13***
|
|
|
|
(0.01)
|
|
time
|
|
-0.45***
|
|
|
|
(0.03)
|
|
R2
|
0.02
|
0.05
|
|
Adj. R2
|
0.02
|
0.05
|
|
Num. obs.
|
10089
|
9873
|
|
RMSE
|
73.29
|
71.94
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
plot2

OLS by Colombia
##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))
col <- mergeiccs %>% filter(idcountry == 170)
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=col, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=col, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=col, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=col, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=col, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
77.16***
|
-268.35***
|
-268.65***
|
|
|
(0.66)
|
(44.89)
|
(44.89)
|
|
s_intrust
|
0.10***
|
0.11***
|
0.16***
|
|
|
(0.01)
|
(0.01)
|
(0.05)
|
|
civic_knowledge
|
-0.07***
|
-0.07***
|
-0.07***
|
|
|
(0.00)
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
0.07
|
0.07
|
|
|
|
(0.05)
|
(0.05)
|
|
s_homelit
|
|
-0.13
|
-0.13
|
|
|
|
(0.07)
|
(0.07)
|
|
s_gender
|
|
-1.28***
|
-1.28***
|
|
|
|
(0.15)
|
(0.15)
|
|
s_poldisc
|
|
-0.03***
|
-0.03***
|
|
|
|
(0.01)
|
(0.01)
|
|
time
|
|
0.17***
|
0.17***
|
|
|
|
(0.02)
|
(0.02)
|
|
s_intrust:civic_knowledge
|
|
|
-0.00
|
|
|
|
|
(0.00)
|
|
R2
|
0.35
|
0.36
|
0.36
|
|
Adj. R2
|
0.35
|
0.36
|
0.36
|
|
Num. obs.
|
11233
|
11038
|
11038
|
|
RMSE
|
83.19
|
82.73
|
82.72
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
62.34***
|
258.87***
|
|
|
(0.59)
|
(55.83)
|
|
civic_knowledge
|
-0.03***
|
-0.03***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
0.24***
|
|
|
|
(0.06)
|
|
s_homelit
|
|
-0.24**
|
|
|
|
(0.09)
|
|
s_gender
|
|
-1.80***
|
|
|
|
(0.18)
|
|
s_poldisc
|
|
0.16***
|
|
|
|
(0.01)
|
|
time
|
|
-0.10***
|
|
|
|
(0.03)
|
|
R2
|
0.04
|
0.08
|
|
Adj. R2
|
0.04
|
0.08
|
|
Num. obs.
|
11262
|
11064
|
|
RMSE
|
105.09
|
103.12
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
plot2

OLS by Dominican Republic
##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))
dom <- mergeiccs %>% filter(idcountry == 214)
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=dom, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=dom, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=dom, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=dom, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=dom, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
74.97***
|
-169.07**
|
-169.51**
|
|
|
(0.85)
|
(60.43)
|
(60.43)
|
|
s_intrust
|
0.13***
|
0.12***
|
0.17**
|
|
|
(0.01)
|
(0.01)
|
(0.05)
|
|
civic_knowledge
|
-0.07***
|
-0.07***
|
-0.07***
|
|
|
(0.00)
|
(0.00)
|
(0.01)
|
|
s_hisced
|
|
0.03
|
0.03
|
|
|
|
(0.08)
|
(0.08)
|
|
s_homelit
|
|
-0.44***
|
-0.44***
|
|
|
|
(0.10)
|
(0.10)
|
|
s_gender
|
|
-0.85***
|
-0.85***
|
|
|
|
(0.21)
|
(0.21)
|
|
s_poldisc
|
|
-0.02
|
-0.02
|
|
|
|
(0.01)
|
(0.01)
|
|
time
|
|
0.12***
|
0.12***
|
|
|
|
(0.03)
|
(0.03)
|
|
s_intrust:civic_knowledge
|
|
|
-0.00
|
|
|
|
|
(0.00)
|
|
R2
|
0.31
|
0.32
|
0.32
|
|
Adj. R2
|
0.31
|
0.32
|
0.32
|
|
Num. obs.
|
7117
|
6655
|
6655
|
|
RMSE
|
48.31
|
47.95
|
47.95
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
71.67***
|
-228.12**
|
|
|
(0.75)
|
(79.93)
|
|
civic_knowledge
|
-0.04***
|
-0.04***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
-0.12
|
|
|
|
(0.10)
|
|
s_homelit
|
|
-0.17
|
|
|
|
(0.13)
|
|
s_gender
|
|
-1.58***
|
|
|
|
(0.27)
|
|
s_poldisc
|
|
0.09***
|
|
|
|
(0.01)
|
|
time
|
|
0.15***
|
|
|
|
(0.04)
|
|
R2
|
0.07
|
0.09
|
|
Adj. R2
|
0.07
|
0.09
|
|
Num. obs.
|
7160
|
6685
|
|
RMSE
|
64.17
|
63.62
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
plot2

OLS by Mexico
##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))
mex <- mergeiccs %>% filter(idcountry == 484)
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=mex, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mex, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mex, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=mex, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mex, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
77.58***
|
-267.31***
|
-276.62***
|
|
|
(0.71)
|
(50.95)
|
(50.97)
|
|
s_intrust
|
0.20***
|
0.19***
|
0.39***
|
|
|
(0.01)
|
(0.01)
|
(0.05)
|
|
civic_knowledge
|
-0.08***
|
-0.08***
|
-0.06***
|
|
|
(0.00)
|
(0.00)
|
(0.01)
|
|
s_hisced
|
|
0.03
|
0.03
|
|
|
|
(0.06)
|
(0.06)
|
|
s_homelit
|
|
-0.21**
|
-0.22**
|
|
|
|
(0.08)
|
(0.08)
|
|
s_gender
|
|
-1.05***
|
-1.05***
|
|
|
|
(0.17)
|
(0.17)
|
|
s_poldisc
|
|
-0.02*
|
-0.02*
|
|
|
|
(0.01)
|
(0.01)
|
|
time
|
|
0.17***
|
0.17***
|
|
|
|
(0.03)
|
(0.03)
|
|
s_intrust:civic_knowledge
|
|
|
-0.00***
|
|
|
|
|
(0.00)
|
|
R2
|
0.40
|
0.41
|
0.41
|
|
Adj. R2
|
0.40
|
0.41
|
0.41
|
|
Num. obs.
|
11583
|
11379
|
11379
|
|
RMSE
|
166.24
|
165.07
|
164.96
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
63.18***
|
-272.13***
|
|
|
(0.57)
|
(58.97)
|
|
civic_knowledge
|
-0.03***
|
-0.03***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
-0.04
|
|
|
|
(0.07)
|
|
s_homelit
|
|
-0.34***
|
|
|
|
(0.09)
|
|
s_gender
|
|
-0.74***
|
|
|
|
(0.20)
|
|
s_poldisc
|
|
0.10***
|
|
|
|
(0.01)
|
|
time
|
|
0.16***
|
|
|
|
(0.03)
|
|
R2
|
0.05
|
0.06
|
|
Adj. R2
|
0.05
|
0.06
|
|
Num. obs.
|
11634
|
11426
|
|
RMSE
|
193.85
|
191.71
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
plot2

OLS by Guatemala 2009
##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))
gtm <- mergeiccs %>% filter(idcountry == 320)
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=gtm, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=gtm, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=gtm, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=gtm, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=gtm, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
74.49***
|
75.83***
|
89.85***
|
|
|
(1.11)
|
(1.32)
|
(3.62)
|
|
s_intrust
|
0.11***
|
0.11***
|
-0.19*
|
|
|
(0.01)
|
(0.01)
|
(0.07)
|
|
civic_knowledge
|
-0.07***
|
-0.07***
|
-0.10***
|
|
|
(0.00)
|
(0.00)
|
(0.01)
|
|
s_hisced
|
|
0.23**
|
0.26***
|
|
|
|
(0.08)
|
(0.08)
|
|
s_homelit
|
|
-0.13
|
-0.13
|
|
|
|
(0.11)
|
(0.11)
|
|
s_gender
|
|
-1.34***
|
-1.36***
|
|
|
|
(0.24)
|
(0.24)
|
|
s_poldisc
|
|
-0.00
|
0.00
|
|
|
|
(0.01)
|
(0.01)
|
|
s_intrust:civic_knowledge
|
|
|
0.00***
|
|
|
|
|
(0.00)
|
|
R2
|
0.35
|
0.36
|
0.36
|
|
Adj. R2
|
0.35
|
0.36
|
0.36
|
|
Num. obs.
|
3798
|
3684
|
3684
|
|
RMSE
|
41.28
|
41.24
|
41.15
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
65.52***
|
58.11***
|
|
|
(0.93)
|
(1.35)
|
|
civic_knowledge
|
-0.04***
|
-0.04***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
-0.17
|
|
|
|
(0.10)
|
|
s_homelit
|
|
-0.26
|
|
|
|
(0.14)
|
|
s_gender
|
|
-0.74*
|
|
|
|
(0.30)
|
|
s_poldisc
|
|
0.12***
|
|
|
|
(0.02)
|
|
R2
|
0.10
|
0.12
|
|
Adj. R2
|
0.10
|
0.12
|
|
Num. obs.
|
3804
|
3690
|
|
RMSE
|
52.42
|
51.66
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading

OLS by Paraguay 2009
##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))
pry <- mergeiccs %>% filter(idcountry == 604)
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=pry, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=pry, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=pry, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=pry, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=pry, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
69.95***
|
71.15***
|
71.25***
|
|
|
(0.84)
|
(1.02)
|
(2.71)
|
|
s_intrust
|
0.10***
|
0.09***
|
0.09
|
|
|
(0.01)
|
(0.01)
|
(0.05)
|
|
civic_knowledge
|
-0.05***
|
-0.06***
|
-0.06***
|
|
|
(0.00)
|
(0.00)
|
(0.01)
|
|
s_hisced
|
|
0.35***
|
0.35***
|
|
|
|
(0.08)
|
(0.08)
|
|
s_homelit
|
|
-0.04
|
-0.04
|
|
|
|
(0.10)
|
(0.10)
|
|
s_gender
|
|
-1.67***
|
-1.67***
|
|
|
|
(0.20)
|
(0.20)
|
|
s_poldisc
|
|
-0.00
|
-0.00
|
|
|
|
(0.01)
|
(0.01)
|
|
s_intrust:civic_knowledge
|
|
|
0.00
|
|
|
|
|
(0.00)
|
|
R2
|
0.35
|
0.36
|
0.36
|
|
Adj. R2
|
0.35
|
0.36
|
0.36
|
|
Num. obs.
|
5034
|
4914
|
4914
|
|
RMSE
|
69.71
|
69.44
|
69.45
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
63.12***
|
55.90***
|
|
|
(0.68)
|
(1.08)
|
|
civic_knowledge
|
-0.03***
|
-0.03***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
0.15
|
|
|
|
(0.11)
|
|
s_homelit
|
|
0.19
|
|
|
|
(0.14)
|
|
s_gender
|
|
-1.75***
|
|
|
|
(0.27)
|
|
s_poldisc
|
|
0.14***
|
|
|
|
(0.01)
|
|
R2
|
0.09
|
0.12
|
|
Adj. R2
|
0.09
|
0.11
|
|
Num. obs.
|
5037
|
4916
|
|
RMSE
|
92.75
|
91.50
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading

OLS by Peru 2016
##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))
per <- mergeiccs %>% filter(idcountry == 600)
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=per, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=per, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=per, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=per, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=per, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#screenreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
70.30***
|
71.54***
|
76.89***
|
|
|
(1.19)
|
(1.40)
|
(4.16)
|
|
s_intrust
|
0.13***
|
0.10***
|
-0.01
|
|
|
(0.01)
|
(0.02)
|
(0.08)
|
|
civic_knowledge
|
-0.06***
|
-0.06***
|
-0.07***
|
|
|
(0.00)
|
(0.00)
|
(0.01)
|
|
s_hisced
|
|
0.15
|
0.15
|
|
|
|
(0.10)
|
(0.10)
|
|
s_homelit
|
|
-0.02
|
-0.01
|
|
|
|
(0.14)
|
(0.14)
|
|
s_gender
|
|
-1.40***
|
-1.41***
|
|
|
|
(0.28)
|
(0.28)
|
|
s_poldisc
|
|
0.01
|
0.01
|
|
|
|
(0.01)
|
(0.01)
|
|
s_intrust:civic_knowledge
|
|
|
0.00
|
|
|
|
|
(0.00)
|
|
R2
|
0.36
|
0.36
|
0.36
|
|
Adj. R2
|
0.35
|
0.36
|
0.36
|
|
Num. obs.
|
2958
|
2756
|
2756
|
|
RMSE
|
36.43
|
36.54
|
36.54
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
63.22***
|
59.21***
|
|
|
(0.92)
|
(1.28)
|
|
civic_knowledge
|
-0.03***
|
-0.03***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
0.05
|
|
|
|
(0.12)
|
|
s_homelit
|
|
0.23
|
|
|
|
(0.17)
|
|
s_gender
|
|
-2.32***
|
|
|
|
(0.34)
|
|
s_poldisc
|
|
0.10***
|
|
|
|
(0.02)
|
|
R2
|
0.07
|
0.10
|
|
Adj. R2
|
0.07
|
0.10
|
|
Num. obs.
|
2968
|
2766
|
|
RMSE
|
45.62
|
44.53
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading
